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Making Gravity Great Again

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  • Martin, Will

Abstract

The gravity model is now widely used for policy analysis and hypothesis testing, but different estimators give sharply different parameter estimates and popular estimators are likely biased because dependent variables are limited-dependent, error variances are nonconstant and missing data frequently reported as zeros. Monte Carlo analysis based on real-world parameters for aggregate trade shows that the traditional Ordinary Least Squares estimator in logarithms is strongly biased downwards. The popular Poisson Pseudo Maximum Likelihood model also suffers from downward bias. An Eaton-Kortum maximum-likelihood approach dealing with the identified sources of bias provides unbiased parameter estimates.
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Suggested Citation

  • Martin, Will, 2020. "Making Gravity Great Again," 2020: Economic Implications of COVID-19, December 14-15, Virtual Platform 339376, International Agricultural Trade Research Consortium.
    • Handle: RePEc:ags:iats20:339376
    DOI: 10.22004/ag.econ.339376
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    Cited by:

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    2. Ian M. Sheldon, 2021. "Reflections on a Career as an Industrial Organization and International Economist," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 43(2), pages 468-499, June.
    3. Mario Larch & Jeff Luckstead & Yoto V. Yotov, 2024. "Economic sanctions and agricultural trade," American Journal of Agricultural Economics, John Wiley & Sons, vol. 106(4), pages 1477-1517, August.

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